scholarly journals Path planning of mobile robot based on particle swarm optimization algorithm

2021 ◽  
Vol 2093 (1) ◽  
pp. 012013
Author(s):  
Huimin Yang ◽  
Lei Jiang ◽  
Lijuan Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Population Diversity ◽  
Convergence Speed ◽  
Mean Deviation ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Induction Strategy ◽  
Speed And Accuracy

Abstract Aiming at the premature convergence problem of particle swarm optimization (PSO) due to the loss of population diversity in the late stage of search, this paper improves the traditional PSO and proposes the elite mean deviation induction strategy, which improves the convergence speed and accuracy of PSO. The experimental results show that the convergence speed and accuracy of the improved PSO are improved. The effectiveness of the improved algorithm is proved.

A Diversity Guided Particles Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1429-1433
Author(s):  
Na Li ◽  
Yuan Xiang Li
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Population Diversity ◽  
Premature Convergence ◽  
Local Optimum ◽  
Convergence Problem ◽  
Swarm Optimization ◽  
Search Capability

A new particle swarm optimization algorithm (a diversity guided particles swarm Optimization), which is guided by population diversity, is proposed. In order to overcome the premature convergence of the algorithm, a metric to measure the swarm diversity is designed, the update of velocity and position of particles is controlled by this criteria, and the four sub-processes are introduced in the process of updating in order to increase the swarm diversity, which enhance to the ability of particle swarm optimization algorithm (PSO) to break away from the local optimum. The experimental results exhibit that the new algorithm not only has great advantage of global search capability, but also can avoid the premature convergence problem effectively.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

A Particle Swarm Optimization Algorithm Based on Simulated Annealing

2014 ◽  
Vol 989-994 ◽  
pp. 2301-2305 ◽  
Author(s):  
Zi Chao Yan ◽  
Yang Shen Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Optimum ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Simulated Analysis ◽  
Metropolis Criterion

The passage aims at solving the problems resulted from the optimized process of Particle Swarm Optimization (PSO), which might reduce the population diversity, cause the algorithm to convergence too early, etc. A whole new mutable simulated annealing particle swarm optimization is proposed based on the combine of the simulated annealing mechanism and mutation. This new algorithm substitutes the Metropolis criterion in the simulated annealing mechanism for mutagenic factors in the process of mutation, which both ensures the diversity of the particle swarm, and ameliorates the quality of the swarm, so that this algorithm would convergence to the global optimum. According to the result of simulated analysis, this hybrid algorithm maintains the simplicity of the particle swarm optimization, improves its capability of global optimization, and finally accelerates the convergence and enhances the precision of this algorithm.

Bacterial Particle Swarm Optimization Algorithm

2011 ◽  
Vol 211-212 ◽  
pp. 968-972
Author(s):  
Ming Li ◽  
Xue Ling Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Optimum ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Bacterial Colony ◽  
Evolutionary Mechanism ◽  
Basic Law

The loss of the population diversity leads to the premature convergence in existing particle swarm optimization(PSO) algorithm. In order to solve this problem, a novel version of PSO algorithm called bacterial PSO(BacPSO), was proposed in this paper. In the new algorithm, the individuals were replaced by bacterial, and a new evolutionary mechanism was designed by the basic law of evolution of bacterial colony. Such evolutionary mechanism also generated a new natural termination criterion. Propagation and death operators were used to keep the population diversity of BacPSO. The simulation results show that BacPSO algorithm not only significantly improves convergence speed ,but also can converge to the global optimum.

A Resilient Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight

2013 ◽  
Vol 712-715 ◽  
pp. 2423-2427
Author(s):  
Zhi Dong Wu ◽  
Sui Hua Zhou ◽  
Shi Min Feng ◽  
Zu Jing Xiao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Optimum ◽  
Local Extremum ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
The Individual

To overcome the shortage that the particle swarm optimization is prone to trap into local extremum searching for the lost in population diversity, a strategy in which the velocity is not dependent on the size of distance between the individual and the optimal particle but only dependent on its direction is proposed. The average similarity of particles in the population is seem as the measure of population diversity and it is used to balance the global and local searching of the algorithm. Based on establishing the relationship between inertia weight and the measure of population diversity which has been inserted into the algorithm, A resilient particle swarm optimization algorithm with dynamically changing inertia weight (ARPSO) was proposed. ARPSO was applied in simulation experiment. The results show that the algorithm has the ability to avoid being trapped in local extremum and advance the probability of finding global optimum.

A novel three layer particle swarm optimization for feature selection

2021 ◽  
pp. 1-15
Author(s):  
Chenye Qiu ◽  
Ning Liu
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Convergence Speed ◽  
Selection Strategy ◽  
Small Subset ◽  
Feature Subset ◽  
Swarm Optimization

Feature selection (FS) is a vital data preprocessing task which aims at selecting a small subset of features while maintaining a high level of classification accuracy. FS is a challenging optimization problem due to the large search space and the existence of local optimal solutions. Particle swarm optimization (PSO) is a promising technique in selecting optimal feature subset due to its rapid convergence speed and global search ability. But PSO suffers from stagnation or premature convergence in complex FS problems. In this paper, a novel three layer PSO (TLPSO) is proposed for solving FS problem. In the TLPSO, the particles in the swarm are divided into three layers according to their evolution status and particles in different layers are treated differently to fully investigate their potential. Instead of learning from those historical best positions, the TLPSO uses a random learning exemplar selection strategy to enrich the searching behavior of the swarm and enhance the population diversity. Further, a local search operator based on the Gaussian distribution is performed on the elite particles to improve the exploitation ability. Therefore, TLPSO is able to keep a balance between population diversity and convergence speed. Extensive comparisons with seven state-of-the-art meta-heuristic based FS methods are conducted on 18 datasets. The experimental results demonstrate the competitive and reliable performance of TLPSO in terms of improving the classification accuracy and reducing the number of features.

An Improved Particle Swarm Optimization Algorithm

2013 ◽  
Vol 401-403 ◽  
pp. 1328-1335 ◽  
Author(s):  
Yu Feng Yu ◽  
Guo Li ◽  
Chen Xu
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Premature Convergence ◽  
High Dimensional ◽  
Convergence Problem ◽  
Swarm Optimization

Particle swarm optimization (PSO) algorithm has the ability of global optimization , but it often suffers from premature convergence problem, especially in high-dimensional multimodal functions. In order to overcome the premature property and improve the global optimization performance of PSO algorithm, this paper proposes an improved particle swarm optimization algorithm , called IPSO. The simulation results of eight unimodal/multimodal benchmark functions demonstrate that IPSO is superior in enhancing the global convergence performance and avoiding the premature convergence problem to SPSO no matter on unimodal or multimodal high-dimensional (100 real-valued variables) functions.

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